An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations

Petzold A, Wessely A, Erdmann M, Schliep S, Schreml S, Rivera Monroy LC, Vera J, Drexler K, Niebel D, Hayani KM, Kiesewetter F, Berking C, Koch E, Heppt M (2025)

Publication Language: English

Publication Type: Journal article

Publication year: 2025

Journal

Virchows Archiv Springer Verlag (Germany)

Book Volume: 487

Pages Range: 1047-1058

DOI: 10.1007/s00428-025-04272-6

Abstract

Cutaneous squamous cell carcinoma (cSCC) and verruca vulgaris (VV) are skin conditions involving the proliferation of epidermal keratinocytes requiring fundamentally different treatments. Histological evaluation of highly differentiated squamous cell proliferations can be challenging, particularly in small or superficial samples. This study aims to improve diagnostic accuracy using an AI model to distinguish cSCC from VV. We developed a deep-learning model using clustering-constrained attention multiple instance learning (CLAM) to classify hematoxylin and eosin-stained whole-slide images (WSIs) as cSCC or VV. The dataset comprised 289 WSIs (n = 148 cSCC, n = 141 VV). Quality control was ensured through expert review: the training cohort was evaluated by four dermatopathologists, and the evaluation cohort by six additional experts. On the training set, the model achieved an AUROC of 0.99, with an accuracy of 94.9% for cSCC and 91.2% for VV. On the evaluation set, it reached an AUROC of 0.96, and accuracies of 82.4% (cSCC) and 97.4% (VV), similar to the average performance of individual dermatopathologists. We successfully trained and implemented an interpretable deep-learning-based weakly supervised model on WSIs distinguishing cSCC from VV, which could enhance AI-supported diagnostics in the future.

Authors with CRIS profile

Anne Petzold Department of Dermatology Anja Wessely Department of Dermatology Michael Erdmann Department of Dermatology Luis Carlos Rivera Monroy Lehrstuhl für Informatik 5 (Mustererkennung) Julio Vera González Professur für Tumorimmunologie (Schwerpunkt Systembiologie) Carola Berking Lehrstuhl für Haut- und Geschlechtskrankheiten Elias Koch Department of Dermatology Markus Heppt Department of Dermatology

Involved external institutions

Universitätsklinikum Regensburg DE Germany (DE) Atrium Health Wake Forest Baptist (prev. Wake Forest Baptist Medical Center) US United States (USA) (US)

How to cite

APA:

Petzold, A., Wessely, A., Erdmann, M., Schliep, S., Schreml, S., Rivera Monroy, L.C.,... Heppt, M. (2025). An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations. Virchows Archiv, 487, 1047-1058. https://doi.org/10.1007/s00428-025-04272-6

MLA:

Petzold, Anne, et al. "An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations." Virchows Archiv 487 (2025): 1047-1058.

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